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Breast cancer is the most common type of cancer and the fifth leading cause of death in women over 40. Therefore, prompt diagnostic and treatment is essential. In this work a TMA Computer Aided Diagnosis (CAD) system has been implemented to provide support to pathologists in their daily work. For that purpose, the tool covers each and every process from the TMA core image acquisition to their individual classification. The first process includes: tissue core location, segmentation and rigid registration of digital microscopic images acquired at different magnifications (5x, 10x, 20x, 20x and 40x) from different devices. The classification process allows performing the core classification selecting different types of color models, texture descriptors and classifiers. Finally, the cores are classified into three categories: malignant, doubtful and benign.

In this paper, we approach the problem of automatically designing fuzzy diagnosis rules for rotating machinery, which can give an appropriate evaluation of the vibration data measured in the target machines. In particular, we explain the implementation to this aim and analyze the advantages and drawbacks of two soft computing techniques: knowledge-based networks (KBN) and genetic algorithms (GA). An application of both techniques is evaluated on the same case study, giving special emphasis to their performance in terms of classification success and computation time.A reduced version of this paper first appeared under the title “A comparative assessment on the application of knowledge-based networks and genetic algorithms to the design of fuzzy diagnosis systems for rotating machinery”, published in the book “Soft Computing in Industry—Recent Appliactions” (Springer Engineering). [Copyright &y& Elsevier]